[Coursera] Machine Learning from Stanford University

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[Coursera] Machine Learning from Stanford University (Size: 1.52 GB)
 avatar.png55.43 KB
 1 - 1 - Welcome (7 min).mp411.95 MB
 1 - 1 - Welcome (7 min).srt9.86 KB
 1 - 2 - What is Machine Learning (7 min).mp49.35 MB
 1 - 2 - What is Machine Learning- (7 min).srt10.14 KB
 1 - 3 - Supervised Learning (12 min).mp413.45 MB
 1 - 3 - Supervised Learning (12 min).srt16.82 KB
 1 - 4 - Unsupervised Learning (14 min).mp416.66 MB
 1 - 4 - Unsupervised Learning (14 min).srt29.06 KB
 docs_slides_Lecture1.pdf3.3 MB
 docs_slides_Lecture1.pptx4.02 MB
 2 - 1 - Model Representation (8 min).mp49 MB
 2 - 1 - Model Representation (8 min).srt9.92 KB
 2 - 2 - Cost Function (8 min).mp49.05 MB
 2 - 2 - Cost Function (8 min).srt9.86 KB
 2 - 3 - Cost Function - Intuition I (11 min).mp412.24 MB
 2 - 3 - Cost Function - Intuition I (11 min).srt12.14 KB
 2 - 4 - Cost Function - Intuition II (9 min).mp411.36 MB
 2 - 4 - Cost Function - Intuition II (9 min).srt11.17 KB
 2 - 5 - Gradient Descent (11 min).mp413.5 MB
 2 - 5 - Gradient Descent (11 min).srt15.39 KB
 3 - 1 - Matrices and Vectors (9 min).mp49.56 MB
 3 - 1 - Matrices and Vectors (9 min).srt15.88 KB
 3 - 1 - Matrices and Vectors (9 min).txt7.08 KB
 3 - 2 - Addition and Scalar Multiplication (7 min).mp47.46 MB
 3 - 2 - Addition and Scalar Multiplication (7 min).srt11.97 KB
 3 - 3 - Matrix Vector Multiplication (14 min).mp415 MB
 3 - 3 - Matrix Vector Multiplication (14 min).srt24.26 KB
 3 - 4 - Matrix Matrix Multiplication (11 min).mp412.59 MB
 3 - 4 - Matrix Matrix Multiplication (11 min).srt20.6 KB
 3 - 5 - Matrix Multiplication Properties (9 min).mp49.81 MB
 4 - 1 - Multiple Features (8 min).mp48.84 MB
 4 - 1 - Multiple Features (8 min).srt14.55 KB
 4 - 2 - Gradient Descent for Multiple Variables (5 min).mp45.78 MB
 4 - 2 - Gradient Descent for Multiple Variables (5 min).srt6.63 KB
 4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp49.46 MB
 4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).srt17 KB
 4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp49.26 MB
 4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).srt18.49 KB
 4 - 5 - Features and Polynomial Regression (8 min).mp48.26 MB
 4 - 5 - Features and Polynomial Regression (8 min).srt15.9 KB
 9 - 1 - Cost Function (7 min).mp47.66 MB
 9 - 1 - Cost Function (7 min).srt13.16 KB
 9 - 2 - Backpropagation Algorithm (12 min).mp413.94 MB
 9 - 2 - Backpropagation Algorithm (12 min).srt22.83 KB
 9 - 3 - Backpropagation Intuition (13 min).mp415.44 MB
 9 - 3 - Backpropagation Intuition (13 min).srt24.96 KB
 9 - 4 - Implementation Note Unrolling Parameters (8 min).mp49.38 MB
 9 - 4 - Implementation Note- Unrolling Parameters (8 min).srt14.9 KB
 9 - 5 - Gradient Checking (12 min).mp413.5 MB
 9 - 5 - Gradient Checking (12 min).srt23.58 KB
 5 - 1 - Basic Operations (14 min).mp417.72 MB
 5 - 1 - Basic Operations (14 min).srt25.35 KB
 5 - 2 - Moving Data Around (16 min).mp420.77 MB
 5 - 2 - Moving Data Around (16 min).srt28.61 KB
 5 - 3 - Computing on Data (13 min).mp415.25 MB
 5 - 3 - Computing on Data (13 min).srt24.95 KB
 5 - 4 - Plotting Data (10 min).mp413.32 MB
 5 - 4 - Plotting Data (10 min).srt17.36 KB
 5 - 5 - Control Statements for while if statements (13 min).mp416.49 MB
 5 - 5 - Control Statements- for, while, if statements (13 min).srt23.38 KB
 6 - 1 - Classification (8 min).mp48.77 MB
 6 - 1 - Classification (8 min).srt16.18 KB
 6 - 2 - Hypothesis Representation (7 min).mp48.34 MB
 6 - 2 - Hypothesis Representation (7 min).srt14.15 KB
 6 - 3 - Decision Boundary (15 min).mp416.74 MB
 6 - 3 - Decision Boundary (15 min).srt26.75 KB
 6 - 4 - Cost Function (11 min).mp413.09 MB
 6 - 4 - Cost Function (11 min).srt22.17 KB
 6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp411.96 MB
 6 - 5 - Simplified Cost Function and Gradient Descent (10 min).srt19.58 KB
 7 - 1 - The Problem of Overfitting (10 min).mp411.15 MB
 7 - 1 - The Problem of Overfitting (10 min).srt19.26 KB
 7 - 2 - Cost Function (10 min).mp411.63 MB
 7 - 2 - Cost Function (10 min).srt19.73 KB
 7 - 3 - Regularized Linear Regression (11 min).mp412 MB
 7 - 3 - Regularized Linear Regression (11 min).srt20.37 KB
 7 - 4 - Regularized Logistic Regression (9 min).mp410.89 MB
 7 - 4 - Regularized Logistic Regression (9 min).srt17.16 KB
 docs_slides_Lecture7.pdf2.34 MB
 docs_slides_Lecture7.pptx2.59 MB
 ex2.zip243.02 KB
 8 - 1 - Non-linear Hypotheses (10 min).mp410.88 MB
 8 - 1 - Non-linear Hypotheses (10 min).srt19.05 KB
 8 - 2 - Neurons and the Brain (8 min).mp49.89 MB
 8 - 2 - Neurons and the Brain (8 min).srt16.41 KB
 8 - 3 - Model Representation I (12 min).mp413.51 MB
 8 - 3 - Model Representation I (12 min).srt21.6 KB
 8 - 4 - Model Representation II (12 min).mp413.45 MB
 8 - 4 - Model Representation II (12 min).srt22.43 KB
 8 - 5 - Examples and Intuitions I (7 min).mp47.89 MB
 8 - 5 - Examples and Intuitions I (7 min).srt13.05 KB
 10 - 1 - Deciding What to Try Next (6 min).mp46.86 MB
 10 - 1 - Deciding What to Try Next (6 min).srt12.44 KB
 10 - 2 - Evaluating a Hypothesis (8 min).mp48.48 MB
 10 - 2 - Evaluating a Hypothesis (8 min).srt11.5 KB
 10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mp414.07 MB
 10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).srt24.64 KB
 10 - 4 - Diagnosing Bias vs. Variance (8 min).mp48.97 MB
 10 - 4 - Diagnosing Bias vs. Variance (8 min).srt16.14 KB
 10 - 5 - Regularization and Bias_Variance (11 min).mp412.6 MB
 10 - 5 - Regularization and Bias_Variance (11 min).srt22.54 KB
 11 - 1 - Prioritizing What to Work On (10 min).mp411.17 MB
 11 - 1 - Prioritizing What to Work On (10 min).srt19.66 KB
 11 - 2 - Error Analysis (13 min).mp415.43 MB
 11 - 2 - Error Analysis (13 min).srt27.47 KB
 11 - 3 - Error Metrics for Skewed Classes (12 min).mp413.25 MB
 11 - 3 - Error Metrics for Skewed Classes (12 min).srt22.06 KB
 11 - 4 - Trading Off Precision and Recall (14 min).mp415.99 MB
 11 - 4 - Trading Off Precision and Recall (14 min).srt28.59 KB
 11 - 5 - Data For Machine Learning (11 min).mp412.87 MB
 11 - 5 - Data For Machine Learning (11 min).srt23.16 KB
 12 - 1 - Optimization Objective (15 min).mp416.65 MB
 12 - 1 - Optimization Objective (15 min).srt29.43 KB
 12 - 2 - Large Margin Intuition (11 min).mp411.81 MB
 12 - 2 - Large Margin Intuition (11 min).srt21.28 KB
 12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp421.83 MB
 12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).srt35.87 KB
 12 - 4 - Kernels I (16 min).mp417.57 MB
 12 - 4 - Kernels I (16 min).srt29.07 KB
 12 - 5 - Kernels II (16 min) (1).mp417.45 MB
 12 - 5 - Kernels II (16 min) (1).srt30.67 KB
 13 - 1 - Unsupervised Learning Introduction (3 min).mp43.8 MB
 13 - 1 - Unsupervised Learning- Introduction (3 min).srt6.99 KB
 13 - 2 - K-Means Algorithm (13 min).mp413.81 MB
 13 - 2 - K-Means Algorithm (13 min).srt26.24 KB
 13 - 3 - Optimization Objective (7 min).mp48.15 MB
 13 - 3 - Optimization Objective (7 min).srt13.7 KB
 13 - 4 - Random Initialization (8 min).mp48.67 MB
 13 - 4 - Random Initialization (8 min).srt16.23 KB
 13 - 5 - Choosing the Number of Clusters (8 min).mp49.4 MB
 13 - 5 - Choosing the Number of Clusters (8 min).srt17.94 KB
 14 - 1 - Motivation I Data Compression (10 min).mp414.31 MB
 14 - 1 - Motivation I- Data Compression (10 min).srt20.14 KB
 14 - 2 - Motivation II Visualization (6 min).mp46.3 MB
 14 - 2 - Motivation II- Visualization (6 min).srt10.18 KB
 14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp410.45 MB
 14 - 3 - Principal Component Analysis Problem Formulation (9 min).srt18.42 KB
 14 - 4 - Principal Component Analysis Algorithm (15 min).mp417.79 MB
 14 - 4 - Principal Component Analysis Algorithm (15 min).srt28.58 KB
 14 - 5 - Choosing the Number of Principal Components (11 min).mp411.84 MB
 14 - 5 - Choosing the Number of Principal Components (11 min).srt21.14 KB
 19 - 1 - Summary and Thank You (5 min).mp46.09 MB
 19 - 1 - Summary and Thank You (5 min).srt8.08 KB
 15 - 1 - Problem Motivation (8 min).mp48.35 MB
 15 - 1 - Problem Motivation (8 min).srt16.02 KB
 15 - 2 - Gaussian Distribution (10 min).mp411.69 MB
 15 - 2 - Gaussian Distribution (10 min).srt20.64 KB
 15 - 3 - Algorithm (12 min).mp413.95 MB
 15 - 3 - Algorithm (12 min).srt23.49 KB
 15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp415.15 MB
 15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).srt27.29 KB
 15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp49.28 MB
 15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).srt16.42 KB
 16 - 1 - Problem Formulation (8 min).mp410.67 MB
 16 - 1 - Problem Formulation (8 min).srt16.82 KB
 16 - 2 - Content Based Recommendations (15 min).mp416.93 MB
 16 - 2 - Content Based Recommendations (15 min).srt28.57 KB
 16 - 3 - Collaborative Filtering (10 min).mp411.75 MB
 16 - 3 - Collaborative Filtering (10 min).srt20.24 KB
 16 - 4 - Collaborative Filtering Algorithm (9 min).mp410.31 MB
 16 - 4 - Collaborative Filtering Algorithm (9 min).srt16.49 KB
 16 - 5 - Vectorization Low Rank Matrix Factorization (8 min).mp49.68 MB
 16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).srt16.31 KB
 17 - 1 - Learning With Large Datasets (6 min).mp46.5 MB
 17 - 1 - Learning With Large Datasets (6 min).srt7.86 KB
 17 - 2 - Stochastic Gradient Descent (13 min).mp415.33 MB
 17 - 2 - Stochastic Gradient Descent (13 min).srt18.15 KB
 17 - 3 - Mini-Batch Gradient Descent (6 min).mp47.32 MB
 17 - 3 - Mini-Batch Gradient Descent (6 min).srt7.78 KB
 17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp413.33 MB
 17 - 4 - Stochastic Gradient Descent Convergence (12 min).srt16.16 KB
 17 - 5 - Online Learning (13 min).mp414.91 MB
 17 - 5 - Online Learning (13 min).srt27.68 KB
 18 - 1 - Problem Description and Pipeline (7 min).mp47.91 MB
 18 - 1 - Problem Description and Pipeline (7 min).srt14.71 KB
 18 - 2 - Sliding Windows (15 min).mp416.52 MB
 18 - 2 - Sliding Windows (15 min).srt31.47 KB
 18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp418.82 MB
 18 - 3 - Getting Lots of Data and Artificial Data (16 min).srt35.19 KB
 18 - 4 - Ceiling Analysis What Part of the Pipeline to Work on Next (14 min).mp416.11 MB
 18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).srt30.5 KB
 docs_slides_Lecture18.pdf1.97 MB
 docs_slides_Lecture18.pptx6.13 MB

Description

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Year : 2012
Manufacturer : Coursera / Stanford University
Website : https://www.coursera.org/course/ml
Author : Andrew Ng, Associate Professor
Duration : 19:53:10
Type of material dispensed : Video
Tutorial Language : English
Description : About the Course Machine learning is the Science of getting Computers to act without being Explicitly Programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn some of About Silicon Valley's Best Practices in Innovation as it pertains to AI and Machine learning. This course Provides A Broad introduction to Machine learning, datamining, statistical and Pattern Recognition.
Topics include:
(i) Supervised learning (parametric / non-parametric algorithms, support vector machines, kernels, neural networks).
(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
(iii) Best practices in machine learning (bias / variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics , audio, database mining, and other areas.

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[Coursera] Machine Learning from Stanford University